AN ECONOMIC ANALYSIS OF DEGRADATION IN THE QUEENSLAND MULGA RANGELANDS pot

A thesis submitted for the degree of Master of Agricultural Economics in the University of Queensland Department of Agriculture August, 1991 iii Abstract The economic issues involved in arid rangeland degradation have become an increasing focus in agricultural economics research world-wide. The economic and social factors which contribute to degradm'on in Queensland's arid mulga rangelandr are explored in this thesis. Many of the region's problems, including the small property size structure, grdng management practices and land tenure have their origins in the historical development of the region. These and other factors are idenn3ed using both a regression analysis of cross- sectional dm and a stochastic dynamic programming model of the rangeland. Regressions pelformed on data j?om parallel economic and land condition surveys of 46 graziers in the south-west Queensland mulga rangelandr are used to establish a link between degradation and land utilisation policy. Land degradation is shown to be more severe on propem'es with higher stocking rates. The importance of property size, financial and domestic cost pressures, land condition and proportion of residual land types in land use decisions are explored. De analysis supports the hypotheses that smaller property sizes and higher interest cost commitments are associated with higher stocking rates. As expected, propem'es with a greater proportion ofpoorer quality land types tend to adopt lower stocking rates. Kangaroo numbers, an effective regional proxy variable, is positively related to stocking rates indicating the tendency for native grazer populations to be higher on more . productive land types. The regression analysis also provides some evidence that incomes are higher on properties with a greater degree of land degradation. While the cross-sectional regression approach is emtive in idennning some of the economic issues, it has distinct shortcomings. Regional biases are diflcult to isolate from the relationships between stocking rates and the various economic factors and much of the variation in stocking rate may in fact be due to regional dzrerences. Moreover, the technique does not adequately consider the intertemporal nature of the rangeland resource problem. iv A stochastic dynamic programming model, amalgamating the cross-sectional swwy data with historical field trial data is developed to address these shortcomings. Ihe @nmic programming model describes a Markov decision process, with stocking rate as the sole land use decision and pasture biomass as the indicator of rangeland condition. For a given pasture biomass and stocking rate, the transition to a'following stare is described by a probability dism'bution derived porn simulated climaric data. By individually varying economic parameters in the model, including property size, wool prices, discount rates and risk, the response in optimal stocking rates and returns can be assessed. The dynamic programming model also generates optimal net present values and shadow prices associated with the marginal usage of pasture biomass corresponding to each optimal decision set. Pasture condition is imputed from the long-term probability distribution of pasture stares generated by the model. _ The dynamic programming model reveals that the small property size structure in the mulga rangelands, largely a legacy of early land a.inistra.tion policy, is a majorpotential factor in land degradation. Graziers with small holdingsjind it economically optimal to stock at higher rates in an efon to achieve economies offlock size. The costs of degradatr'on incurred by higher stocking strategies are balanced by savings porn more encient management. l2e model shows that at the average wool prices assumed, an area of at least 35,000 hectares is required to produce positive net present values at all levels of pasture biomass. I The effect of wool prices on optimal land use rates reflects the non-linear relationship between wool quality and price, which makes finer wools relatively more attractive during periods of high prices. Graziers can gain by opportunistically stocking at higher rates to induce finer wool. The so-called lfine wool effect' can be achieved by felling mulga for supplementary feed. The individual grazier's discount rate is expected to vary according to financial circumstances, planning horizon and arn'n.de to risk. Sensitivity analysis of the model revealed that optimal stocking rates increased as the grazier's discount rate rose, reflecting a decreasing concern for resource conservation. The amtude of graziers to risk is firther This thesis reports the original work of the author, except as otherwise stated. It has not been submitted previously for a degree at any university. analysed in a utility-maximising version of the dynamic programming model. Quadratic, negative exponential and spliced utility Bnctions are used to convert monetary outcomes to un'lity values. Generally, a more risk-averse attitude infers more conservative stocking rates. Apart from examining the importance of propeny size, wool prices and discount rate in land degradation, the study seeks to validate the use of dynamic programming analysis in policy issues where intertemporal elements are of central importance. The problems of dimensionality, data requirements and compurational limits which hamper the efectiveness of dynamic programming as a decision-making tool are less resmktive of its usefllness in examining policy concern. The dynamic programming method has firther potential uses in identBing minimum property sizes for long-tern viability and in the analysis of the response of land prices to degradation. vii CONTENTS ABSTRACT LIST OF TABLES LIST OF FIGURES xii 1. MULGA RANGELAND DEGRADATION - AN INTRODUCTION 1.1. Defining degradation 1.2. Degradation of the mulga rangelands 1.2.1. The degradation cycle 1.2.2. Extent of mulga rangeland degradation 1.3. Theory and analytical issues: A synopsis e 2. ECONOMICS OF LAND DEGRADATION 13 A historical perspective Early grazing management a;, Closer settlement policy and property size A market failure approach to land degradation Imperfect market for information and research Divergence between private and social discount rate Intergenerational equity issues and market failure Financial pressures Ownership intentions and planning horizon Uncertainty about irreversibility Property rights and land tenure Land tenure in the mulga rangelands . Land market imperfections Externalities Government policy failure Characteristics of region and production system Drought management and mulga top-feeding Native and feral grazing animals The effect of stocking rate on wool quality Summary C 3. ANALYSIS OF ECONOMIC FACTORS INFLUENCING LAND DEGRADATION IN THE MULGA RANGELANDS 3 1 3.1. Hypotheses 3.2. Survey data 3.2.1. Survey methods 3.2.2. Economic data viii Land condition data Techniques of analysis The relationship between stocking rates and degradation A land degradation variable Analysis Economic factors influencing stocking rates Identification of economic variables ' Analysis A two-stage least squares approach Property incomes and stocking rates Analysis of land prices and degradation Land price analysis for the mulga region Intra-regional comparison Inter-regional comparison C 4. INTERTEMPORAL OPTIMISATION OF RESOURCE USE - THEORY AND APPLICATION The mulga rangeland resource and its characteristics A model of renewable resource use Maximum sustainable yield A simple graphical intertemporal model Dynamic optimisation methods Optimal control theory Dynamic programming Application of dynamic optimisation methods Review of dynamic optimisation literature 5. A STOCHASTIC DYNAMIC PROGRAMMING MODEL FOR THE MULGA RANGELANDS The model framework Determination of state variables Indicators of rangeland productivity Pasture biomass Basal area Selected state variables and partitions Determination of decision variables Selected decision variable The transition matrix Methods for deriving the transition matrix Pasture biomass transition matrix The stage return function Property sizes Wool cut per head Wool quality Wool price Stock replacement and values 5.5.5.1. Mortalities 5.5.5.2. Normal replacements 5.5.5.3. Stock adjustment between seasons 5.5.6. Variable production costs 5.5.7. Variable marketing costs 5.5.8. Fixed costs 5.5.9. Discount rates 5.6. An alternative specification - A constrained choice model C 6. A DYNAMIC ANALYSIS OF ECONOMIC INFLUENCES ON OPTIMAL STOCKING RATES Effects of property size Optimal stocking rates Optimal net present value function Shadow prices Long-run probabilities of pasture states Effects of changing model parameters Wool price variations Wool quality differentials Discount rates A utility maximising model Constrained choice model Optimal net present values and shadow prices Sensitivity to property size and discount rate Summary CH 7. CONCLUSIONS 7.1. Economic factors 7.2. The role of dynamic optimisation techniques 7.3. Policy issues . ACKNO S RE CES APPENDIX I - APPENDIX I1 - APPENDIX m - APPENDIX IV - APPENDIX V - APPENDIX VI - APPENDIX VII - APPENDIX VIII - Survey Data Collection Techniques Pasture Biomass Transition Matrix Arabella Trial Data Wool Price Series Matrix of Stage Return Components Sheep Mortality Rates Wool Price Sensitivity Analysis The GPDP Computer Package APPENDIX REFERENCES LIST OF TABLES Table 3.1. Summary of Economic Data from a Survey of Mulga Rangeland Graziers, Average per Property. Table 3.2. Summary of Land Condition Data from a Survey of Mulga Rangeland Graziers, (per cent of total random points). Table 3.3. OLS Regressions of Land Degradation Variables on Stocking Rate. Table 3.4. Regressions of Economic and Physical Variables on Stocking Rate. Table 3.5. OLS Regressions of Economic and Physical Variables on Stocking Rates, excluding Residual Land Types. Table 3.6. Land Degradation and Stocking Rate - A Two-Stage Least Squares Approach. Table 3.7. Significance of Property Size and Land Condition in Explaining Property Incomes. Table 5.1. Average Pasture Biomass for Land Classes, 1982 Land Condition Survey. Table 5.2. Pasture Biomass Partitions and Midpoints Table 5.3. Stocking Rate Decisions and Partitions Table 5.4. Pasture Biomass Regressions for Deriving the Transition Matrix, Double-Log Functional Form. , Table 5.5. Economic Characteristics of Property Size Groups. Table 5.6. Wool Cut Per Head for Different Rates of Pasture Utilisation (Arabella Trial) Table 5.7. Average Wool Prices for Fibre Diameter, 1973174 to 1989190, in 1987188 Dollars. Table 5.8. Costs of Mulga Feeding. Table 5.9. Average Saleyard Prices for Young and Aged Wethers at Dalby (1987188 Dollars). Table 5.10. Penalty Costs for Stock Adjustment over Time. xi Table 5.11. Labour and Materials Costs per DSE, 1987188 Dollars. Table 5.12. Variable Production Costs used in Estimating Stage Returns. Table 5.13. Wool Marketing Charges. Table 5.14. Fixed Costs. Table 6.1. Effect of Property Size on Optimal Stocking Rates. Table 6.2. Effect of Property Size on Optimal Net Present Values at Full Equity. Table 6.3. Effect of Property Size on Optimal Net Present Values with Average Interest Costs Included. Table 6.4. Effect of Wool Prices on Optimal Stocking Rates. Table 6.5. Effect of Wool Prices on Net Present Values per Hectare. Table 6.6. Effect of Wool Quality Differentials on Optimal Stocking Rates. Table 6.7. Effect of Discount Rates on Optimal Stocking Rates. Table 6.8. Optimal Stocking Rates using Quadratic &d Negative Exponential Utility Functions. Table 6.9. Optimal Stocking Rates for a Risk-Averse Grazier at Four Equity Levels, Spliced Utility Function. Table 6.10. Effect of Property Size on Optimal Stocking Rate ~djukrnents. Table 6.11. Effect of Discount Rate on Optimal Stocking Rate Adjustments. [...]... land value, degradation of arid lands is generally irreversible Treatment of the cause rather than the symptom is therefore crucial Economic analysis has a major role in determining the causes and the appropriate policies to prevent or slow the process of degradation The broad objectives of this thesis are: - - to identify the economic and financial constraints in decisions on' land use in Queensland' s... woody weed infestation and gullying The visible evidence of economic or management influence is the 'fence-line effect' along property boundaries The mulga rangelands comprise some 19 million hectares in the south-west corner of Queensland (Figure 1.1) The rangelands support a significant pastoral industry accounting for about one quarter of annual wool production in Queensland and one twelfth of the beef... in a survey by ee Skinner and Kelsey in 1964 They reported evidence of sheet erosion and increased run-off in the western mulga rangelands and concluded that deterioration had occurred during the previous 20 to 30 years The Western Arid Region Land Use Studies (WARLUS), undertaken in the 1960s and 1970s by the Queensland Department of Primary Industries, also identified degradation in the mulga rangelands. .. relevant to the mulga rangelands include imperfect markets for information, divergence between private and social discount rates, property rights and land tenure, and land market imperfections An analysis of market failure arguments and those relevant to the mulga rangelands follows in Chapter 2, together with a historical perspective on the degradation problem The role of stocking rate in land degradation. .. starting point 2.1 A historical ~erspectivq 2.1.1 and The Queensland mulga rangelands were first settled in the 1 8 6 0 ~ ~ by the 1870s, most of the better grazing land had been claimed Due to an over-optimistic assessment of the grazing capability of the rangeland, many of the smaller landholders soon encountered financial difficulties Their problems were exacerbated by dingoes, isolation, drought and... almost half of the past 25 years (Mills et al., 1989; Mills, 1989) The mulga rangelands comprise two broad land zones, the soft mulga and the hard mulga, distinguished by rainfall, soil and vegetation characteristics Generally, the hard mulga lies to the west of Charleville and the Warrego River, stretching west of Quilpie, while the soft mulga lies to the east The soft mulga zone consists of flat or gently... entirely removed from the system Further symptoms of degradation are reduced infiltration and greater water run-off An increase in the number of flows of the Paroo River, the catchment of which lies almost wholly within the western mulga rangelands, is evidence of greater run-off This occumed despite relatively constant rainfall patterns (Mdes, 1990) Ten-year moving averages of the number of annual Paroo River... summarises the significant economic issues in mulga rangeland degradation in the context of the sustainability debate The usefulness of dynamic optimisation techniques in identifymg the focus of rangeland policy is discussed - Chapter 2 DE :,' ATION The degradation of rangeland from the pristine state through stable states of woody weed ;s infestation to eventual irreversible sheet erosion was attributed in. .. in mulga rangeland degradation may fall into the category of market failure, others are best described as government policy failure, or merely characteristics of the rangeland production system Moreover, many of the problems of land degradation have their origins in the historical development of the region and early grazing management A review of the historical background to degradation is therefore... Physical Factors in the Land Degradation Cycle Figure 32 Actual Land Prices in the Eastern and Western Mulga Rangelands Compared to the Prices Paid Index, 1961-89 Figure 33 Actual Land Prices in the Western Mulga Rangelands and the Blackall District Compared to the Prices Paid Index, 1968-88 Figure 41 The Resource Growth - Stock Relationship and Maximum Sustainable Yield re 42 The Intertemporal Optimum . AN INTRODUCTION 1.1. Defining degradation 1.2. Degradation of the mulga rangelands 1.2.1. The degradation cycle 1.2.2. Extent of mulga rangeland. quality Summary C 3. ANALYSIS OF ECONOMIC FACTORS INFLUENCING LAND DEGRADATION IN THE MULGA RANGELANDS 3 1 3.1. Hypotheses 3.2. Survey data